when i try to convert a yolov3 single channel model, some errors occur. When i run bash 1_quantize_model.sh,
File “tensorflow_core/python/framework/op_def_library.py”, line 793, in _apply_op_helper
File “tensorflow_core/python/util/deprecation.py”, line 507, in new_func
File “tensorflow_core/python/framework/ops.py”, line 3360, in create_op
File “tensorflow_core/python/framework/ops.py”, line 3429, in _create_op_internal
File “tensorflow_core/python/framework/ops.py”, line 1773, in init
File “tensorflow_core/python/framework/ops.py”, line 1613, in _create_c_op
ValueError: Depth of input (1) is not a multiple of input depth of filter (32) for ‘convolution_0_2/Conv2D’ (op: ‘Conv2D’) with input shapes: [1,418,418,1], [3,3,32,32].
[7819] Failed to execute script tensorzonex
@Frank I am a little bit confused of what you said, in my opinion if i set –channel-mean-value to ‘0 0 0 256’ the pre-process will normalize image data to the range [0,1].
@objectness You say Thanks for your quick response.My data is normalized to the range [0, 1], so i set the –channel-mean-value paras to ‘0 0 0 256’.Any other suggestions . I understand that it has been normalized before the conversion…
This parameter configuration should be correct.
Can you try to convert the official weights file? It will help you to confirm whether there is a problem with your conversion environment
@Frank I have already tried both official weights and my own weights, while channels=3 converting process is fine, nbg_unify_yolov3 file is generated, but when i use a single channel, i met some errors.
@Frank I have successful trained a single channel model on FLIR dataset, which can work fine on original repo. I have also converted the model to caffe framework, .pb file also could work fine.
@Frank Maybe i should not use AlexeyAB’s version, in his repo d.X.cols = hwc. But it’s weird because 3 channels model can work fine while single channel model cannot.